English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Attribution methods calculate attributions that visually explain thepredictions of deep neural networks (DNNs) by highlighting important parts ofthe input features. In particular, gradient-based attribution (GBA) methods arewidely used because they can be easily implemented through automaticdifferentiation. In this study, we use the attributions that filter outirrelevant parts of the input features and then verify the effectiveness ofthis approach by measuring the classification accuracy of a pre-trained DNN.This is achieved by calculating and applying an \textit{attribution mask} tothe input features and subsequently introducing the masked features to the DNN,for which the mask is designed to recursively focus attention on the parts ofthe input related to the target label. The accuracy is enhanced under a certaincondition, i.e., \textit{no implicit bias}, which can be derived based on ourtheoretical insight into compressing the DNN into a single-layer neuralnetwork. We also provide Gradient\,*\,Sign-of-Input (GxSI) to obtain theattribution mask that further improves the accuracy. As an example, on CIFAR-10that is modified using the attribution mask obtained from GxSI, we achieve theaccuracy ranging from 99.8\% to 99.9\% without additional training.

Attribution Mask: Filtering Out Irrelevant Features By Recursively Focusing Attention on Inputs of DNNs